AI-based screening of Clostridium acetobutylicum with high furfural tolerance and butanol production

Advances in strain breeding for butanol biosynthesis were quite limited because of physiological complexity of solventogenic Clostridia. Using AI, this study developed a high-throughput screening method for Clostridium acetobutylicum to find strains with inhibitor tolerance and high butanol production. A mutant library was generated from C. acetobutylicum ATCC 824 through ARTP mutagenesis and physiological traits were digitized using color indicators. The classification performance of Machine learning algorithms (PCA, PLS, SVM, ANN) were compared for different butanol-producing strains. Among 2000 strains screened, C. acetobutylicum Tust-f3 was identified, which could tolerate 4.5 g/L furfural and yield 10.5 g/L butanol from undetoxified lignocellulosic hydrolysate. Proteome analysis reveals that 38 proteins may play a crucial role. Subsequently, seven universal detoxification components for furfural were identified via heterologous expression in E. coli Genes CA_RS19590 and CA_RS08810 showed significant growth improvement (14.44 and 14.28-fold, respectively, compared to control). This study highlights the potential of machine learning in strain selection and breeding.